In many applications, various communications systems and especially in multi-carrier modulation systems there are requests for non-linear modification of a signal because multi-carrier signals suffer from a high-Peak-to-Average Ratio (PAR). Examples of such multi-carrier systems are Orthogonal Frequency Division Multiplexing (OFDM), Digital Audio Broadcasting (DAB) or Digital Video Broadcasting (DVB) to mention only a few. In many cases, such non-linear modifications have to be kept within a certain bandwidth or within certain spectral mask restrictions. In particular radio signal applications, this ensures that the output signal does not spill over into adjacent channels or exceeds spectral emission limits.
One typical example of such non-linear modification is PAR reduction. PAR reduction increases efficiency and average output power of a peak power limited Power Amplifier (PA). A large PAR brings disadvantages like a reduced efficiency of a Radio Frequency (RF) power amplifier and an increased complexity of analogue to digital and digital to analogue converters. The objective of peak reduction techniques is therefore to reduce the peak amplitude excursions of the output signal while keeping the spectrum expansion within specified limits, such as spectral mask and adjacent channel power ratio (ACPR) specifications, and keeping in-band error within specified limits, so-called error vector magnitude (EVM) specification.
There are many existing prior art solutions dealing with peak power reduction for multi-carrier signals and signal carrier signals.
One prior art approach for reducing the peak power of an input waveform is to implement power clipping. In the power clipping approach, whenever the amplitude of the input signal is lower than a predetermined threshold, the input signal is passed to the output unchanged, and whenever the amplitude of the input signal exceeds the threshold, the output signal is clamped to the threshold level. Of course, the clipping operation destroys some of the information contained in the original signal. However, the user should be able to tolerate this loss of information as along as the threshold is kept sufficiently high.
Decresting is another prior art approach for reducing the peak power of an input waveform, while avoiding the overshooting problems caused by the baseband filter in the power clipper. In this approach, which is suggested in the international patent application WO 03/001697, an error signal is created that represents the amount by which the input signal exceeds a threshold. This error signal is then subtracted from the original input signal in order to form a decrested output signal.
Tone reservation is another method used to reduce peak power of a signal, typically used when an input signal is a multi-carrier signal or a multi-tone signal. In this method, described in J. Tellado-Mourello. “Peak to Average Reduction For Multicarrier Modulation” Dept. of Electrical Engineering of Standford University, pp. 66-99, September 1999, the peak power is reduced by selecting or reserving a subset of a plurality of frequencies that constitute a multi-carrier symbol. These selected or reserved frequencies are used to create an appropriate impulse function, which is scaled, shifted, rotated and subtracted from the input multi-tone signal at each peak of the input signal that exceeds a predetermined threshold. Thus, one or several peaks may be clipped in this fashion and in a single iteration. However, reducing one or more peaks may cause the resulting waveform to exceed the clipping threshold at other positions. Therefore, the process is repeated until a satisfactory peak-to-average reduction is achieved. The impulse function created from the subset of reserved frequencies are usually pre-computed since the subset of reserved frequencies is usually known in advance.
However, when non-linear processing as described in the above prior art forces a signal, such as a time-discrete signal, to stay within certain boundaries, this can generally only be guaranteed at sample instants. As the time-discrete signal (i.e. from digital form) is converted into time-continuous form (i.e into analogue form), peak regrowth occurs and therefore some limiting is needed in the analogue part of the system.
The traditional solution to this problem is to perform from the start the non-linear processing at a sufficiently high rate. In other words, peak regrowth can be avoided if a sufficiently high Over-Sampling Ratio (OSR) is used when starting processing the time-discrete signal. For example, in the tone reservation approach, typically four or higher OSR is usually used to make sure that peak regrowth is effectively avoided. This means that the computational complexity increases. In practical designs, the increase in computational cost is directly proportional to the OSR, and if an OSR of 4 is used, the computational cost increases by a factor of 4 and therefore a substantial increase in hardware and power consumption of a transmitter.
In the pending international patent application PCT/SE2006/050256, a solution is proposed that considerably lowers the computational complexity. In this proposed solution, even though a low OSR (lower than 4) is used, peak regrowth is effectively reduced. This is achieved by applying a fractional sample shift on an output signal from one or several successive processing stages. The basic idea of applying a fractional sample shift on a signal is to delay the signal by a fraction of a sample in or between each processing stage, so that signal samples used in a later stage are placed in-between the sample instants used in a previous stage.
FIG. 1 illustrates the solution proposed in the above mentioned pending application. As shown, a multi-stage non-linear processing of an input main signal 1 is performed. In a first processing stage 10, time-discrete samples of a multi-carrier signal are used as input values. These samples have a certain sample rate and thus a certain inter-sample spacing. Based on a predetermined threshold level A, also known as a clipping level, information on samples exceeding this threshold level is found by passing time-discrete samples of the input signal 1 through a peak finder 11. The information (110, 120) on sample or samples exceeding the threshold level includes: the size of the overshooting part exceeding the threshold level, the phase and the time position of the sample/samples of the overshooting part.
This information (110, 120) is further used to manipulate a kernel previously stored in block 12. The kernel is usually constructed from peak reduction frequencies (or reserved frequencies/tones) of the multi-carrier input signal.
Referring back to FIG. 1, the manipulation of the kernel in block 12, includes a rotation of the kernel signal based on the phase of overshooting part; a scaling of the kernel signal based on the size of the overshooting part, and a shifting of the kernel signal based on the time position of sample/samples of the overshooting part. After manipulation of the kernel signal, the overshooting part of the input signal is reduced by combining the manipulated kernel signal 2 with a delayed version 3 of the input signal. The above mentioned procedure to reduce a peak of the input signal can be repeated X times, depending on the requirement of the system. After that X peak reduction steps have been performed, a fractional sample shift 20 is applied on the peak reduced signal 4. The benefit of using a fractional sample shifting of the signal 4 is to allow a subsequent processing stage 10 to find and reduce peaks that may hide “in-between” samples thus eliminating the need to use a high OSR. The input signal used in the solution described above may be a multi-carrier signal, e.g. an OFDM signal.
Although the computational complexity is effectively reduced using the solution described in the pending application, the randomness of the peaks; in number, size and fractional position; makes it difficult to divide the peak reducing effort into regular batches at different stages. In other words, the randomness of the peaks renders it difficult to exploit the peak reducing effort in a more efficient way.
In addition, choosing a fixed number of peak reducing steps before entering a subsequent stage is suboptimal, since this will either mean that too few peaks are reduced at some stages, or that more peaks than necessary are reduced at most stages. In such a fixed scheme, a certain amount of extra peak reducing steps are required at each stage to make sure that all relevant peaks are reduced prior to entering the next processing stage. The introduction of extra peak reducing steps will therefore require additional computational load.
Furthermore, choosing to end the peak reducing steps at some specific quality level is also suboptimal since the number of peak reducing steps at a certain processing stage will vary between signal blocks (e.g. OFDM blocks). For some blocks, the computational resources will run out in an early stage, so that the later processing stages fail in contributing to the peak reduction. This will result in peak re-growth in the output signal.